Evaluation of cryptocurrency price prediction using lstm and cnns models

NS Wen, LS Ling - JOIV: International Journal on Informatics Visualization, 2023 - joiv.org
Cryptocurrencies created by Nakamoto in 2009 have gained significant interest due to their
potential for high returns. However, the cryptocurrency market's unpredictability makes it …

[HTML][HTML] Development of prediction model for information technology equipment procurement as the basis of knowledge for an Intelligent Decision Support System …

NU Maulidevi, VG Christianto, E Hikmawati… - Resources, Environment …, 2024 - Elsevier
The high quality of Information Technology (IT) equipment undoubtedly contributes to the
seamless functioning of various industries in today's digital era. As organizations strive to …

Flowscope: Enhancing decision making by time series forecasting based on prediction optimization using hybridflow forecast framework

NS Boyeena, BS Kumar - arxiv preprint arxiv:2411.10716, 2024 - arxiv.org
Time series forecasting is crucial in several sectors, such as meteorology, retail, healthcare,
and finance. Accurately forecasting future trends and patterns is crucial for strategic planning …

Modeling Temperature In The Ecuadorian Paramo Through Deep Learning

MJC Cabay, JA Piedra, RM Ayala - IEEE Journal of Selected …, 2025 - ieeexplore.ieee.org
This study focuses on predicting temperature using multivariate time series at two locations
in Tungurahua province, Ecuador: Mula Corral, situated in the high-altitude grassland …

[HTML][HTML] A Stock Prediction Method Based on Multidimensional and Multilevel Feature Dynamic Fusion

Y Dong, Y Hao - Electronics, 2024 - mdpi.com
Stock price prediction has long been a topic of interest in academia and the financial
industry. Numerous factors influence stock prices, such as a company's performance …

A Traffic Model Integrating Long Short-Term Memory Networks with Multi-class Macroscopic Equations

K Binjaku, C Pasquale, S Sacone… - 2024 IEEE 20th …, 2024 - ieeexplore.ieee.org
Accurate traffic state estimation is crucial for effective transportation management and urban
planning. In recent years, deep learning techniques, particularly Long Short-Term Memory …

Product Demand Forecast Analysis Using Predictive Models and Time Series Forecasting Algorithms on the Temu Marketplace Platform

MN Trisolvena, M Masruroh… - … Software Engineering and …, 2024 - lembagakita.org
In the rapidly evolving digital era, the ability to accurately forecast product demand is crucial
for marketplace platforms like Temu. Demand uncertainty can lead to issues such as …

A Study of LSTM Optimisation for Forecasting Volatile Time Series Data

DG Taslim, H Tjahyadi… - 2024 4th International …, 2024 - ieeexplore.ieee.org
Forecasting time series data is an important subject in economics and business where the
Autoregressive Integrated Moving Average (ARIMA) has been extensively used despite its …

A Framework for Enhanced Time Series Forecasting of Internet Traffic Based on AdaBoost-LSTM Integration

Q Zhang - 2024 5th International Conference on Computer …, 2024 - ieeexplore.ieee.org
Internet traffic is conceptualized as a form of time series data, making algorithms designed
for time series forecasting applicable for predicting dynamics of internet traffic. This study …

Decision support in a volatile electricity market: forecasting and cost optimization

OA Olsson - 2023 - lup.lub.lu.se
Given the increase in electricity prices in recent years due to two reasons; the rebound effect
after the initial corona outbreak and the Russian invasion of Ukraine, the burden of paying …